Visual Analysis of Multidimensional Categorical Datasets

We present a set of interactive techniques for the visual analysis of multidimensional categorical data. Our approach is based on Multiple Correspondence Analysis (MCA), which allows one to analyze relationships, patterns, trends and outliers among dependent categorical variables. We use MCA as a dimensionality reduction technique to project both observations and their attributes in the same 2D space. We use a treeview to show attributes and their domains, a histogram of their representativity in the dataset, and as a compact overview of attribute-related facts. A second view shows both attributes and observations. We use a Voronoi diagram whose cells can be interactively merged to discover salient attributes, cluster values, and bin categories. Barchart legends help assigning meaning to the 2D view axes and 2D point clusters. We illustrate our techniques with real-world application data.

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